Experiment Overview

A master’s student did a big experiment looking at the effect of pre-adaptation and diversity of a focal species on community composition. The experiment was done in soil using a LacZ marked strain of Pseudomonas fluorescens.

Some Pseudomonas fluorescens were pre-adapted to the compost before being put into experimental microcosms with the resident community. Clones were then isolated from these pre-adaptation treatments by plating onto LacZ agar. They were then left for 6 weeks and at the end of the experiment all samples were squenced using Amplicon 16S sequencing. They got put through a sequencing pipeline using dada2 in R and the phylogenetic tree was constructed using Fasttree.

To tease apart pre-adaptation and diversity, there were a variety of treatments:

Table summary of the experimental setup and treatments

d <- tibble::tibble(treatment = c("individual_clone", "individual_clone", "4_related_clones", "4_related_clones", "4_unrelated_clones",  "evolved_without_community", "evolved_with_community", "lacz_ancestor", "negative_control", "nmc_t0"),
            evolved_with_community = c('yes', 'no', 'yes', 'no', 'NA', 'no', 'yes', 'no', 'NA', 'NA'),
            reps = c(24, 24, 6, 6, 12, 6, 6, 6, 6, 1))
knitr::kable(d)
treatment evolved_with_community reps
individual_clone yes 24
individual_clone no 24
4_related_clones yes 6
4_related_clones no 6
4_unrelated_clones NA 12
evolved_without_community no 6
evolved_with_community yes 6
lacz_ancestor no 6
negative_control NA 6
nmc_t0 NA 1

You can see that this is a somewhat unbalanced design, but as we are using permutational tests this should be ok (I think).

Looking at changes in community composition

1. Looked at whether Pseudomonas fluorescens persists in the treatments

knitr::include_graphics(c('../sequencing/plots/fresh/prop_pseudomonas.png'))

2. Look at changes in community composition with preadaptation history and diversity of Pseudomonas fluorescens

Next we looked at how community composition may change with diversity and with preadaptation history (with and without the resident community). To do this we kept only 4_related_clones, individual_clone and evolved_with_community and evolved_without_community to give us a fully factorial design of treatments. We dropped factors that had no pre-adaptation stage (lacz_ancestor) and that were mixed between evolved with and without the community (4_unrelated_clones) and that had no additional Pseudomonas fluorescens added (negative_control).

knitr::include_graphics(c('../sequencing/plots/fresh/effect_of_evol_history.png'))

3. Look specifically at diversity to see if diversity of clones changes the impact on the community

Because there seems to be an effect of diversity overall, higher diversity is closer to the natural microbial community and individual clone is closer to the lacz ancestor, we looked at changes in the position of centroids across levels of diversity (number of clones)

Had several different levels:

d <- readRDS('../sequencing/data/output/mult_comp.rds')

knitr::kable(d)
X1 X2 R2 pval pvalBon pvalFDR pvalHolm
C_1 C_24 0.0693591 0.0029 0.0174 0.017 0.0174
C_1 C_4 0.0078370 0.6580 3.9480 0.658 0.6580
C_1 C_high 0.0779211 0.0034 0.0204 0.010 0.0174
C_24 C_4 0.0950678 0.0146 0.0876 0.029 0.0584
C_24 C_high 0.1329260 0.0336 0.2016 0.040 0.0672
C_4 C_high 0.1262032 0.0169 0.1014 0.025 0.0584

Single clones are change community composition differently to the negative control and the 24 clone samples. There are no other significant differences in community composition with levels of diversity.

knitr::include_graphics(c('../sequencing/plots/fresh/PCoA_plot_diversity.png'))

Look at changes in alpha diversity across levels of preadaptation history and diversity

Calculated loads of diversity metrics (i.e. Shannon, Simpsons, Observed OTU) and pielou’s evenness.

1. Look at how diversity and evenness change through treatments

knitr::include_graphics(c('../sequencing/plots/fresh/alpha_diversity.png'))

Alpha diversity seems a bit weird. Richness is largest in nmc_t0 (makes sense), but the negative control has the biggest reduction in observed OTUs (alongside evolved_without_community).

Not sure what to make of this…

Look at abundance of Pseudomonads across samples

1. Try and identify Pseudomonas fluorescens

knitr::include_graphics(c('../sequencing/plots/fresh/alex_pseudomonas_tree.png'))

knitr::include_graphics(c('../sequencing/plots/fresh/alex_pseudomonas_distance.png'))

2. How abundant are Pseudomonads and this sequence (SBW25)

knitr::include_graphics(c('../sequencing/plots/fresh/pseudomonas_abundance.png'))

knitr::include_graphics(c('../sequencing/plots/fresh/SBW25_prop.png'))

knitr::include_graphics(c('../sequencing/plots/fresh/prop_pseudomonas.png'))

knitr::include_graphics(c('../sequencing/plots/fresh/pseudomonas_diversity.png'))

Other Qs answered previously and possible avenues to go down

Extra plots

knitr::include_graphics(c('../sequencing/plots/fresh/ind_clone_fitness.png'))

knitr::include_graphics(c('../sequencing/plots/predict_multiclones.png'))